@inproceedings{radford-2021-case,
    title = "{CASE} 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models",
    author = "Radford, Benjamin J.",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali},
    booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.case-1.25/",
    doi = "10.18653/v1/2021.case-1.25",
    pages = "203--207",
    abstract = "We introduce a method for the classification of texts into fine-grained categories of sociopolitical events. This particular method is responsive to all three Subtasks of Task 2, Fine-Grained Classification of Socio-Political Events, introduced at the CASE workshop of ACL-IJCNLP 2021. We frame Task 2 as textual entailment: given an input text and a candidate event class ({``}query''), the model predicts whether the text describes an event of the given type. The model is able to correctly classify in-sample event types with an average F1-score of 0.74 but struggles with some out-of-sample event types. Despite this, the model shows promise for the zero-shot identification of certain sociopolitical events by achieving an F1-score of 0.52 on one wholly out-of-sample event class."
}